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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function

ABSTRACT Building accurate and flexible galaxy–halo connection models is crucial in modelling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and that the local environment of the halo can be an excellent indicator of galaxy assembly bias. In this paper, we propose an extended halo occupation distribution (HOD) model that includes both a concentration-based assembly bias term and an environment-based assembly bias term. We use this model to achieve a good fit (χ2/degrees of freedom = 1.35) on the 2D redshift-space two-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample. We find that the inclusion of both assembly bias terms is strongly favoured by the data and the standard five-parameter HOD model is strongly rejected. More interestingly, the redshift-space 2PCF drives the assembly bias parameters in a way that preferentially assigns galaxies to lower mass haloes. This results in galaxy–galaxy lensing predictions that are within 1σ agreement with the observation, alleviating the perceived tension between galaxy clustering and lensing. We also showcase a consistent 3σ–5σ preference for a positive environment-based assembly bias that persists over variations in the fit. We speculate that the environmental dependence might be driven by underlying processes such as mergers and feedback, but might also be indicative of a larger halo boundaries such as the splashback radius. Regardless, this work highlights the importance of building flexible galaxy–halo connection models and demonstrates the extra constraining power of the redshift-space 2PCF.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evidence of galaxy assembly bias in SDSS DR7 galaxy samples from count statistics

We present observational constraints on the galaxy–halo connection, focusing particularly on galaxy assembly bias from a novel combination of counts-in-cylinders statistics, P(N CIC ), with the standard measurements of the projected two-point correlation function w p (r p ), and number density n gal of galaxies. We measure n gal , w p (r p ), and P(N CIC ) for volume-limited, luminosity-threshold samples of galaxies selected from SDSS DR7, and use them to constrain halo occupation distribution (HOD) models, including a model in which galaxy occupation depends upon a secondary halo property, namely halo concentration. We detect significant positive central assembly bias for the M r < -20.0 and M r <-19.5 samples. Central galaxies preferentially reside within haloes of high concentration at fixed mass. Positive central assembly bias is also favoured in the M r < -20.5 and M r < -19.0 samples. We find no evidence of central assembly bias in the M r < -21.0 sample. We observe only a marginal preference for negative satellite assembly bias in the M r < -20.0 and M r < -19.0 samples, and non-zero satellite assembly bias is not indicated in other samples. Our findings underscore the necessity of accounting for galaxy assembly bias when interpreting galaxy survey data, and demonstrate the potential of count statistics in extracting information from the spatial distribution of galaxies, which could be applied to both galaxy–halo connection studies and cosmological analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The effect of dust bias on the census of neutral gas and metals in the high-redshift Universe due to SDSS-II quasar colour selection

ABSTRACT We present a systematic study of the impact of a dust bias on samples of damped Ly α absorbers (DLAs). This bias arises as an effect of the magnitude and colour criteria utilized in the Sloan Digital Sky Survey (SDSS) quasar target selection up until data release 7 (DR7). The bias has previously been quantified assuming only a contribution from the dust obscuration. In this work, we apply the full set of magnitude and colour criteria used up until SDSS-DR7 in order to quantify the full impact of dust biasing against dusty and metal-rich DLAs. We apply the quasar target selection algorithm on a modelled population of intrinsic colours, and by exploring the parameter space consisting of redshift, ($z_{\rm{\small QSO}}$and zabs), optical extinction, and H i column density, we demonstrate how the selection probability depends on these variables. We quantify the dust bias on the following properties derived for DLAs at z ≈ 3: the incidence rate, the mass density of neutral hydrogen and metals, and the average metallicity. We find that all quantities are significantly affected. When considering all uncertainties, the mass density of neutral hydrogen is underestimated by 10–50 per cent, and the mass density in metals is underestimated by 30–200 per cent. Lastly, we find that the bias depends on redshift. At redshift z = 2.2, the mass density of neutral hydrogen and metals might be underestimated by up to a factor of 2 and 5, respectively. Characterizing such a bias is crucial in order to accurately interpret and model the properties and metallicity evolution of absorption-selected galaxies.

Krogager, Jens-Kristian↗
DOE OSTI2022

Genetic programming for understanding cognitive biases that generate polarization in social networks

Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.

Gunaratne, Chathika↗
DOE OSTI2016

Global Climate Impacts of Fixing the Southern Ocean Shortwave Radiation Bias in the Community Earth System Model (CESM)

Here, a large, long-standing, and pervasive climate model bias is excessive absorbed shortwave radiation (ASR) over the midlatitude oceans, especially the Southern Ocean. This study investigates both the underlying mechanisms for and climate impacts of this bias within the Community Earth System Model, version 1, with the Community Atmosphere Model, version 5 [CESM1(CAM5)]. Excessive Southern Ocean ASR in CESM1(CAM5) results in part because low-level clouds contain insufficient amounts of supercooled liquid. In a present-day atmosphere-only run, an observationally motivated modification to the shallow convection detrainment increases supercooled cloud liquid, brightens low-level clouds, and substantially reduces the Southern Ocean ASR bias. Tuning to maintain global energy balance enables reduction of a compensating tropical ASR bias. In the resulting preindustrial fully coupled run with a brighter Southern Ocean and dimmer tropics, the Southern Ocean cools and the tropics warm. As a result of the enhanced meridional temperature gradient, poleward heat transport increases in both hemispheres (especially the Southern Hemisphere), and the Southern Hemisphere atmospheric jet strengthens. Because northward cross-equatorial heat transport reductions occur primarily in the ocean (80%), not the atmosphere (20%), a proposed atmospheric teleconnection linking Southern Ocean ASR bias reduction and cooling with northward shifts in tropical precipitation has little impact. In summary, observationally motivated supercooled liquid water increases in shallow convective clouds enable large reductions in long-standing climate model shortwave radiation biases. Of relevance to both model bias reduction and climate dynamics, quantifying the influence of Southern Ocean cooling on tropical precipitation requires a model with dynamic ocean heat transport.

58 GEOSCIENCES↗
DOE OSTI2018

Precipitation and Moisture in Four Leading CMIP5 Models: Biases across Large-Scale Circulation Regimes and Their Attribution to Dynamic and Thermodynamic Factors

As key variables in general circulation models, precipitation and moisture in four leading models from CMIP5 (phase 5 of the Coupled Model Intercomparison Project) are analyzed, with a focus on four tropical oceanic regions. It is found that precipitation in these models is overestimated in most areas. However, moisture bias has large intermodel differences. The model biases in precipitation and moisture are further examined in conjunction with large-scale circulation by regime-sorting analysis. Results show that all models consistently overestimate the frequency of occurrence of strong upward motion regimes and peak descending regimes of 500-hPa vertical velocity $ω500$. In a given $ω500$ regime, models produce too much precipitation compared to observation and reanalysis. But for moisture, their biases differ from model to model and also from level to level. Furthermore, error causes are revealed through decomposing contribution biases into dynamic and thermodynamic components. For precipitation, the contribution errors in strong upward motion regimes are attributed to the overly frequent $ω500$. In the weak upward motion regime, the biases in the dependence of precipitation on $ω500$ and the $ω500$ probability density function (PDF) make comparable contributions, but often of opposite signs. On the other hand, the biases in column-integrated water vapor contribution are mainly due to errors in the frequency of occurrence of $ω500$, while thermodynamic components contribute little. These findings suggest that errors in the frequency of $ω500$ occurrence are a significant cause of biases in the precipitation and moisture simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bias Corrected NOAA HRRR Wind Resource Data for Grid Integration Applications

To address the need for regularly updated wind resource data, NREL has processed the High-Resolution Rapid Refresh (HRRR) outputs for use in grid integration modeling. The HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). Several barriers have prevented the HRRR's widespread proliferation in the wind energy industry: missing timesteps (prior to 2019), challenging file format for wind energy analysis, limited vertical height resolution, and negative bias versus legacy WIND Toolkit data (2007-2013). NREL has applied re-gridding, interpolation, and bias-correction to the native HRRR data to overcome these limitations. This results in the now-publicly-available bias corrected and interpolated HRRR (BC-HRRR) dataset for weather years 2015 to 2023. Bias correction is necessary for wind resource consistency across weather years to be used simultaneously in planning-focused grid integration studies alongside the original WIND Toolkit data. We show that quantile mapping with the WIND Toolkit as a historical baseline is an effective method for bias correcting the interpolated HRRR data: the BC-HRRR has reduced mean bias versus comparable gridded wind resource datasets (+0.12 m/s versus Vortex) and has very low mean bias versus ground measurement stations (+0.01 m/s) (Buster et al., 2024). BC-HRRR's consistency with the legacy WIND Toolkit allows NREL to extend grid integration analysis to 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. As with historical datasets like the WIND Toolkit, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling) both independently and alongside the legacy WIND Toolkit.

Array↗
DOE OSTI2025

How Do Uncertainties in Galaxy Formation Physics Impact Field-level Galaxy Bias?

Our ability to extract cosmological information from galaxy surveys is limited by uncertainties in the galaxy–dark matter halo relationship for a given galaxy population, which are governed by the intricacies of galaxy formation. To quantify these uncertainties, we examine quenched and star-forming galaxies using two distinct approaches to modeling galaxy formation: U$\scriptsize{NIVERSE}$M$\scriptsize{ACHINE}$, an empirical semianalytic model, and the I$\scriptsize{llustris}$TNG hydrodynamical simulation. We apply a second-order hybrid N-body perturbative bias expansion to each galaxy sample, enabling direct comparison of modeling approaches and revealing how uncertainties in the galaxy–halo connection affect bias parameters and non-Poisson noise across number densities and redshifts. Notably, we find that quenched and star-forming galaxies occupy distinct parts of the bias parameter space, and that the scatter induced from these different galaxy formation models is small when conditioned on similar selections of galaxies. We also detect the signature of assembly bias in our samples; this leads to small but significant deviations from analytic bias predictions, while assembly bias-removed samples match these predictions well. This work indicates that galaxy samples from a spectrum of reasonable, physically motivated models for galaxy formation give a relatively small range of field-level galaxy bias parameters. We estimate a set of priors from these models that should be useful in extracting cosmological constraints from luminous red galaxy– and emission line galaxy–like samples. Looking forward, careful estimates of the range of impacts of galaxy formation, for a given sample and cosmological analysis, will be an essential ingredient for extracting the most precise cosmological information from current and future large galaxy surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

MEASURING TYPE IA SUPERNOVA POPULATIONS OF STRETCH AND COLOR AND PREDICTING DISTANCE BIASES

Simulations of Type Ia supernovae (SNe Ia) surveys are a critical tool for correcting biases in the analysis of SNe Ia to infer cosmological parameters. Large-scale Monte Carlo simulations include a thorough treatment of observation history, measurement noise, intrinsic scatter models, and selection effects. In this Letter, we improve simulations with a robust technique to evaluate the underlying populations of SN Ia color and stretch that correlate with luminosity. In typical analyses, the standardized SN Ia brightness is determined from linear “Tripp” relations between the light curve color and luminosity and between stretch and luminosity. However, this solution produces Hubble residual biases because intrinsic scatter and measurement noise result in measured color and stretch values that do not follow the Tripp relation. We find a 10 σ bias (up to 0.3 mag) in Hubble residuals versus color and 5 σ bias (up to 0.2 mag) in Hubble residuals versus stretch in a joint sample of 920 spectroscopically confirmed SN Ia from PS1, SNLS, SDSS, and several low- z surveys. After we determine the underlying color and stretch distributions, we use simulations to predict and correct the biases in the data. We show that removing these biases has a small impact on the low- z sample, but reduces the intrinsic scatter σ {sub int} from 0.101 to 0.083 in the combined PS1, SNLS, and SDSS sample. Past estimates of the underlying populations were too broad, leading to a small bias in the equation of state of dark energy w of Δ w = 0.005.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model

Abstract. As a long-standing problem in climate models, large positive shortwave radiation biases exist at the surface over the Southern Ocean, impacting the accurate simulation of sea surface temperature, atmospheric circulation, and precipitation. Underestimations of low-level cloud fraction and liquid water content are suggested to predominantly contribute to these radiation biases. Most model evaluations for radiation focus on summer and rely on satellite products, which have their own limitations. In this work, we use surface-based observations at Macquarie Island to provide the first long-term, seasonal evaluation of both downwelling surface shortwave and longwave radiation in the Australian Community Climate and Earth System Simulator Atmosphere-only Model version 2 (ACCESS-AM2) over the Southern Ocean. The capacity of the Clouds and the Earth’s Radiant Energy System (CERES) product to simulate radiation is also investigated. We utilize the novel lidar simulator, the Automatic Lidar and Ceilometer Framework (ALCF), and all-sky cloud camera observations of cloud fraction to investigate how radiation biases are influenced by cloud properties. Overall, we find an overestimation of +9.5±33.5 W m−2 for downwelling surface shortwave radiation fluxes and an underestimation of -2.3±13.5 W m−2 for downwelling surface longwave radiation in ACCESS-AM2 in all-sky conditions, with more pronounced shortwave biases of +25.0±48.0 W m−2 occurring in summer. CERES presents an overestimation of +8.0±18.0 W m−2 for the shortwave and an underestimation of -12.1±12.2 W m−2 for the longwave in all-sky conditions. For the cloud radiative effect (CRE) biases, there is an overestimation of +4.8±28.0 W m−2 in ACCESS-AM2 and an underestimation of -7.9±20.9 W m−2 in CERES. An overestimation of downwelling surface shortwave radiation is associated with an underestimated cloud fraction and low-level cloud occurrence. We suggest that modeled cloud phase is also having an impact on the radiation biases. Our results show that the ACCESS-AM2 model and CERES product require further development to reduce these radiation biases not just in shortwave and in all-sky conditions, but also in longwave and in clear-sky conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1991

On the sea-state bias of the Geosat altimeter

The sea-state bias in a satellite altimeter's range measurement is caused by the influence of ocean waves on the radar return pulse; it results in an estimate of sea level that is too low according to some function of the wave height. This bias is here estimated for Geosat by correlating collinear differences of altimetric sea-surface heights with collinear differences of significant wave heights (H1/3). Corrections for satellite orbit error are estimated simultaneously with the sea-state bias. Based on twenty 17-day repeat cycles of the Geosat Exact Repeat Mission, the solution for the sea-state bias is 2.6 + or - 0.2 percent of H1/3. The least-squares residuals, however, show a correlation with wind speed U, so the traditional model of the bias has been supplemented with a second term: H1/3 + alpha-2H1/3U. This second term produces a small, but statistically significant, reduction in variance of the residuals. Both systematic and random errors in H1/3 and U tend to bias the estimates of alpha-1 and alpha-2, which complicates comparisons of the results with ground-based measurements of the sea-state bias.

Ray, Richard D.↗
NASA NTRS1994

Bias and high-order galaxy correlation functions in the APM galaxy survey

On large scales, the higher order moments of the mass distribution, S(sub J) = bar-zeta(sub J)/bar-zeta(sup J-1)(sub 2), e.g., the skewness S(sub 3) and kurtosis S(sub 4), can be predicted using nonlinear perturbation theory. Comparison of these predictions with moments of the observed galaxy distribution probes the bias between galaxies and mass. Applying this method to models with initially Gaussian fluctuations and power spectra P(k) similar to that of galaxies in the Automatic Plate Measuring (APM) survey, we find that the predicted higher order moments S(sub J)(R) are in good agreement with those directly inferred from the APM survey in the absence of bias. We use this result to place limits on the linear and nonlinear bias parameters. Models in which the extra power observed on large scales (with respect to the standard cold dark matter (CDM) model) is produced by scale-dependent bias match the APM higher order amplitudes only if nonlinear bias (rather than nonlinear gravity) generates the observed higher order moments. When normalized to Cosmic Background Explorer Differential Microwave Radiometer (COBE DMR), these models are siginificantly ruled out by the S(sub 3) observations. The cold plus hot dark matter model normalized to COBE can reproduce the APM higher order correlations if one introduces nonlinear bias terms, while the low-density CDM model with a cosmological constant does not require any bias to fit the large-scale amplitudes.

Gaztanaga, Enrique↗
NASA NTRS2000

Sound Absorption of a 2DOF Resonant Liner with Negative Bias Flow

This report describes an experimental study conducted to determine the effect of negative bias flow on the sound absorption of a two degree-of-freedom liner. The backwall for the liner was designed to act as a double-Helmholtz resonator so as to act as a hard wall at all frequencies except at its resonant frequencies. All normal incident impedance data presented herein was acquired in an impedance tube. The effect of bias flow is investigated for a buried septum porosity of 2% and 19.5% for bias flow orifice mach numbers up to 03 11. As a porous backwall is needed for the flow to pass through, the effect of bias flow on this backwall all had to be evaluated first. The bias flow appears to modify the resistance and reactance of the backwall alone at lower frequencies up to about 2 kHz, with marginal effects at higher frequencies. Absorption coefficients close to unity are achieved for a frequency range of 500-4000 Hz for the overall liner for a septum porosity of 2% and orifice mach number of 0.128. Insertion loss tests performed in a flow duct facility for grazing flow Mach numbers up to 0.2 and septum mach numbers up to 0.15 showed that negative bias flow can increase insertion loss by as much as 10 dB at frequencies in the range of 500 - 1400 Hz compared to no grazing flow. The effectiveness of the negative bias flow is diminished as the grazing flow velocity is increased.

Ahuja, K. K.↗
NASA NTRS2005

AGCM Biases in Evaporation Regime: Impacts on Soil Moisture Memory and Land-Atmosphere Feedback

Because precipitation and net radiation in an atmospheric general circulation model (AGCM) are typically biased relative to observations, the simulated evaporative regime of a region may be biased, with consequent negative effects on the AGCM s ability to translate an initialized soil moisture anomaly into an improved seasonal prediction. These potential problems are investigated through extensive offline analyses with the Mosaic land surface model (LSM). We first forced the LSM globally with a 15-year observations-based dataset. We then repeated the simulation after imposing a representative set of GCM climate biases onto the forcings - the observational forcings were scaled so that their mean seasonal cycles matched those simulated by the NSIPP-1 (NASA Global Modeling and Assimilation Office) AGCM over the same period-The AGCM s climate biases do indeed lead to significant biases in evaporative regime in certain regions, with the expected impacts on soil moisture memory timescales. Furthermore, the offline simulations suggest that the biased forcing in the AGCM should contribute to overestimated feedback in certain parts of North America - parts already identified in previous studies as having excessive feedback. The present study thus supports the notion that the reduction of climate biases in the AGCM will lead to more appropriate translations of soil moisture initialization into seasonal prediction skill.

Mahanama, Sarith P. P.↗
NASA NTRS2007

Sampling Biases in MODIS and SeaWiFS Ocean Chlorophyll Data

Although modem ocean color sensors, such as MODIS and SeaWiFS are often considered global missions, in reality it takes many days, even months, to sample the ocean surface enough to provide complete global coverage. The irregular temporal sampling of ocean color sensors can produce biases in monthly and annual mean chlorophyll estimates. We quantified the biases due to sampling using data assimilation to create a "truth field", which we then sub-sampled using the observational patterns of MODIS and SeaWiFS. Monthly and annual mean chlorophyll estimates from these sub-sampled, incomplete daily fields were constructed and compared to monthly and annual means from the complete daily fields of the assimilation model, at a spatial resolution of 1.25deg longitude by 0.67deg latitude. The results showed that global annual mean biases were positive, reaching nearly 8% (MODIS) and >5% (SeaWiFS). For perspective the maximum interannual variability in the SeaWiFS chlorophyll record was about 3%. Annual mean sampling biases were low (<3%) in the midlatitudes (between -40deg and 40deg). Low interannual variability in the global annual mean sampling biases suggested that global scale trend analyses were valid. High latitude biases were much higher than the global annual means, up to 20% as a basin annual mean, and over 80% in some months. This was the result of the high solar zenith angle exclusion in the processing algorithms. Only data where the solar angle is <75deg are permitted, in contrast to the assimilation which samples regularly over the entire area and month. High solar zenith angles do not facilitate phytoplankton photosynthesis and consequently low chlorophyll concentrations occurring here are missed by the data sets. Ocean color sensors selectively sample in locations and times of favorable phytoplankton growth, producing overestimates of chlorophyll. The biases derived from lack of sampling in the high latitudes varied monthly, leading to artifacts in the apparent seasonal cycle from ocean color sensors. A false secondary peak in chlorophyll occurred in May-August, which resulted from lack of sampling in the Antarctic.

Gregg, Watson W.↗
NASA NTRS2005

Mechanically Biased, Hinged Pairs of Piezoelectric Benders

The upper part of the figure depicts an actuator that comprises two mechanically biased piezoelectric benders hinged together at their ends and equipped with tabs at their mid-length points for attachment to the relatively moving objects that are to be actuated. In the example of the figure, the attachment tabs are labeled to indicate that the actuator is used to drive a pump piston relative to a base plate. Actuators of this type could be used to drive low-power, small-volume pumps in consumer, medical, and aerospace applications, and to generate and measure linear displacements in such robotic applications as teleoperation and tactile feedback. Each bender is a bimorph a unitary plate that comprises an upper and a lower piezoelectric layer plus electrode layers. Benders may also be made of several layers arranged to produce the same effect at the lower operating voltages. As stated above, each bender is mechanically biased; it is fabricated to have a small permanent curvature (the bias curvature) in the absence of applied voltage. As on other bimorphs, the electrical connections on each bender are arranged so that an applied voltage of suitable polarity causes the upper layer to expand and the lower layer to contract. In this case, the net effect of applying the voltage is that the plate becomes more concave as viewed from below. Conversely, an applied voltage of the opposite polarity causes the plate to become less concave as viewed from below. The benders in a hinged pair are oriented with their bias curvatures concave inward, so that there is a bias distance between the attachment tabs. The two benders are connected electrically in parallel, with their connection polarities chosen so that an applied voltage of one polarity causes both benders to become more convex inward (more bent), while an applied voltage of the opposite polarity causes both benders to become less convex inward (less bent). An increase or decrease in bend is accompanied by an increase or decrease in distance between the attachment tabs; this increase or decrease is the linear displacement desired for actuation. Because the displacement can be either positive or negative relative to the bias distance, depending on the polarity of the applied voltage, the overall stroke achievable for a given magnitude of applied voltage is double the stroke achievable in the absence of mechanical bias. Each hinged pair can be regarded as a unit cell that can serve as a building block for a larger actuator: Multiple unit cells can be stacked (mechanically connected in series), as shown in the lower part of the figure, and electrically connected in parallel to multiply the overall stroke achievable at a given applied voltage.

Sager, Frank E.↗
NASA NTRS2012

System and circuitry to provide stable transconductance for biasing

An amplifier system can include an input amplifier configured to receive an analog input signal and provide an amplified signal corresponding to the analog input signal. A tracking loop is configured to employ delta modulation for tracking the amplified signal, the tracking loop providing a corresponding output signal. A biasing circuit is configured to adjust a bias current to maintain stable transconductance over temperature variations, the biasing circuit providing at least one bias signal for biasing at least one of the input amplifier and the tracking loop, whereby the circuitry receiving the at least one bias signal exhibits stable performance over the temperature variations. In another embodiment the biasing circuit can be utilized in other applications.

Garverick, Steven L.↗
NASA NTRS2012

Correction Technique for Raman Water Vapor Lidar Signal-Dependent Bias and Suitability for Water Wapor Trend Monitoring in the Upper Troposphere

The MOHAVE-2009 campaign brought together diverse instrumentation for measuring atmospheric water vapor. We report on the participation of the ALVICE (Atmospheric Laboratory for Validation, Interagency Collaboration and Education) mobile laboratory in the MOHAVE-2009 campaign. In appendices we also report on the performance of the corrected Vaisala RS92 radiosonde measurements during the campaign, on a new radiosonde based calibration algorithm that reduces the influence of atmospheric variability on the derived calibration constant, and on other results of the ALVICE deployment. The MOHAVE-2009 campaign permitted the Raman lidar systems participating to discover and address measurement biases in the upper troposphere and lower stratosphere. The ALVICE lidar system was found to possess a wet bias which was attributed to fluorescence of insect material that was deposited on the telescope early in the mission. Other sources of wet biases are discussed and data from other Raman lidar systems are investigated, revealing that wet biases in upper tropospheric (UT) and lower stratospheric (LS) water vapor measurements appear to be quite common in Raman lidar systems. Lower stratospheric climatology of water vapor is investigated both as a means to check for the existence of these wet biases in Raman lidar data and as a source of correction for the bias. A correction technique is derived and applied to the ALVICE lidar water vapor profiles. Good agreement is found between corrected ALVICE lidar measurments and those of RS92, frost point hygrometer and total column water. The correction is offered as a general method to both quality control Raman water vapor lidar data and to correct those data that have signal-dependent bias. The influence of the correction is shown to be small at regions in the upper troposphere where recent work indicates detection of trends in atmospheric water vapor may be most robust. The correction shown here holds promise for permitting useful upper tropospheric water vapor profiles to be consistently measured by Raman lidar within NDACC (Network for the Detection of Atmospheric Composition Change) and elsewhere, despite the prevalence of instrumental and atmospheric effects that can contaminate the very low signal to noise measurements in the UT.

MOHAVE-2009↗